Utilizing Event-Related Potential and Behavioral Data to Identify Persistent Low Academic Performance Across the K–1 Transition

• In Event: 72.032 - Data-Driven Approaches in Educational Neuroscience

Abstract

Most experts recommend early identification and remediation for young children performing below average in reading and math. To this end, we investigated 1) when low performers can be reliably identified, 2) the stability of performance across the schooling transition, and 3) whether we can predict stable low performance using behavioral and neurological data. Cluster analyses identified low, average, and high performers in math and reading. We find that 1) low reading skills may be reliably identified in kindergarten, and low math skills may be identified in first grade; 2) a third of low-performers in kindergarten exhibit stable low performance into first grade; and 3) it remains generally unclear why some students experience stable low-performance from kindergarten to first grade.

Authors

• Adrienne D. Woods, University of Michigan

• Sammy F Ahmed, University of Michigan

• Benjamin Katz, University of Michigan - Ann Arbor

• Nicholas Edward Waters, The University of Michigan

• Zachary Salander, University of Massachusetts - Amherst

• Frederick J. Morrison, University of Michigan